Generative Engine Optimization: How a Regional Home Services Provider Built 5 GEO Components to Win AI Search

The Challenge: Invisible in AI Search

When a midwest metro home services provider evaluated why their online leads were plateauing, the answer wasn't in Google Analytics. It was in the shift happening above the search results. ChatGPT, Perplexity, and Google's AI Overviews were now answering questions about local window treatment costs, installation timelines, and product comparisons — and this provider's content wasn't being cited. Competitors with better-structured pages were capturing that visibility instead.

The root problem was content architecture. The provider's existing pages were prose-heavy, with valuable pricing information, local expertise signals, and product comparisons buried in unstructured paragraphs. Large language models couldn't reliably extract those answers. There was no FAQPage schema, no semantic Q&A structure, and no E-E-A-T signals that AI systems could identify as indicators of local authority. The site was well-optimized for 2019's search landscape — not 2025's AI-first reality.

Key Metrics at a Glance

Our Approach: Engineering Content for AI Extraction

BFM's generative engine optimization strategy begins with a foundational question: how does an AI system decide what to cite? The answer involves three factors — semantic clarity (does the content signal its own purpose?), structural accessibility (is the content in the DOM and parseable?), and authority signals (does the page demonstrate E-E-A-T for the topic and location?). Every GEO component we engineered for this client addressed at least one of those factors. The five-component library was designed to work as a system, not a collection of isolated features.

The strategic decision to build reusable React components — rather than per-page content edits — was driven by scale requirements. With 66 service locations and multiple product categories, any manual approach was untenable. By building GEO optimization into the component layer, every new programmatic page automatically inherited AI-optimized structure. This meant the 1,122 location pages all launched with full GEO coverage from day one, not as a retrofit project.

Implementation Deep Dive: The 5 GEO Components

The GEO component library was built in two phases. Phase one delivered the five core extraction-optimized components. Phase two integrated JSON-LD schema generation at the page level, ensuring every page produced machine-readable structured data alongside the human-readable content. Each component was built with a clear, singular GEO purpose — and the entire library exports from a single index file for clean integration across all page templates.

The TLDRSection component was the most direct GEO element — a visually distinct aside element with an aria-label of 'Quick Summary' that signals extractable content to both AI crawlers and human scanners. Each instance contained a single, citable paragraph with specific local facts: service area, pricing context, and a call to action. The QuestionSections component rendered each Q&A as an h3 question with a paragraph answer, mirroring the structure LLMs use to match queries to content. Critically, the accordion implementation kept all answer content in the DOM even when visually collapsed, ensuring crawlability regardless of user interaction.

The LocalExpertise component addressed the hardest GEO challenge for local businesses: proving geographic authority to an AI system. By wrapping location-specific content in a dedicated section with a MapPin icon, an Award signal, and a heading pattern of 'Why [Location] Homeowners Choose Us,' the component gave AI clear semantic indicators of local relevance. The BenefitsTable and ComparisonTable components completed the library by providing machine-readable structured data that AI systems can cite when answering product comparison or decision-making queries.

Technical Architecture: Schema at Scale

Deploying FAQPage schema across 1,122 pages is a different engineering problem than deploying it on a handful of pages. The solution was dynamic JSON-LD generation at build time, pulling location-specific question and answer data from the same data sources that populated the visible page content. Each page received a unique schema object — not a template copy — meaning every FAQPage schema contained genuinely location-specific questions and answers. This is critical because AI systems can detect and deprioritize duplicate or low-value schema markup.

The programmatic architecture started from 15 original pages and expanded to 1,175+ total pages — a 7,733% increase. The 66 location areas that previously had no dedicated pages were each given full product-location page coverage, representing a 2,100% expansion in location coverage. Every page in the new architecture was built with the full GEO component stack, native schema generation, and a Lighthouse-optimized template that maintained 95+ scores across the board. The build pipeline produced the entire site in 60 seconds, enabling rapid iteration without performance tradeoffs.

Results & Impact: What GEO Actually Delivered

The quantifiable output of the GEO implementation is clear: 5 components deployed across 1,122 programmatic pages, with FAQPage schema, LocalBusiness schema, and AggregateRating schema present on every page. The 129-component React architecture that powers the full site maintained a 95+ Lighthouse score throughout the expansion, confirming that AI optimization and performance optimization are not in conflict when built correctly. Location coverage grew by 2,100%, converting 66 previously unrepresented service areas into fully indexed, AI-readable content hubs.

The page growth from 15 to 1,175+ represents a 7,733% expansion of the provider's indexable footprint. Each of those new pages carries a phone conversion value of $150 per call, making the GEO-optimized location pages a direct revenue channel — not just a visibility play. The 60-second build time means the team can push content updates, add new location pages, or modify GEO components site-wide without deployment friction, enabling continuous optimization as AI search behavior evolves.

Before & After: The Full Transformation

The contrast between the pre- and post-implementation state is most visible at the structural level. Before GEO, the site had 15 pages built for human readers using prose-heavy content with no machine-readable structure. After GEO, 1,175+ pages serve both human readers and AI extraction systems with equal effectiveness. The architecture change didn't sacrifice readability — it added AI-legible layers on top of natural language content, a design principle BFM refers to as 'natural language with strategic structure.'

Lessons Learned: What the GEO Build Taught Us

The most important lesson from this implementation is that generative engine optimization is an infrastructure investment, not a content marketing project. The decision to build GEO into the component layer — rather than optimizing individual pages — is what made the 1,122-page deployment possible. If GEO had been treated as a content task, the team would have been manually editing pages for months. By treating it as an engineering task, five components did the work across the entire site simultaneously.

The second major lesson involves accordion implementation details. Not all accordion components behave the same way in the DOM. Standard implementations that hide collapsed content from the DOM effectively remove that content from AI extraction — a serious GEO failure mode that's easy to miss during development. Testing pages directly in ChatGPT and Perplexity before and after implementation is now a standard quality assurance step in BFM's GEO process. Schema alone is insufficient if the underlying HTML content is inaccessible to crawlers.

The GEO Playbook: What Other Home Services Providers Should Know

The window for establishing AI search authority in local home services is still open — but it's narrowing. AI systems like ChatGPT and Perplexity develop citation patterns early, and providers whose content is consistently well-structured and locally authoritative will earn the default citation position for their service areas. This provider's GEO implementation gives them that structural advantage across 66 locations — a moat that competitors without similar architecture will struggle to close quickly.

For home services providers considering a generative engine optimization investment, the key insight from this case study is that the five-component model is replicable. TLDRSection, QuestionSections, LocalExpertise, BenefitsTable, and ComparisonTable address the complete surface area of AI extraction patterns for service-based local businesses. The schema layer — FAQPage, LocalBusiness, AggregateRating — is the machine-readable complement to those human-readable components. Together, they form a complete GEO AI search optimization stack that scales with programmatic page generation.